Data Engineer I

Job Overview

We are seeking an entry-level Data Engineer I to join the Information Technology function within the Data Engineering & Architecture job family. This role is designed for a 2026 graduate with a bachelor's degree in Artificial Intelligence and Machine Learning or a closely related discipline. The position will support the design, development, testing, and operation of reliable data pipelines and curated data products used for analytics, reporting, and AI/ML solutions. The ideal candidate combines strong programming and SQL fundamentals with curiosity, structured problem-solving, attention to data quality, and a willingness to learn modern cloud data engineering practices in a collaborative environment.

Roles & Responsibility:

• Assist in developing, testing, deploying, and monitoring batch and near-real-time data pipelines under the guidance of senior data engineers and architects.
• Ingest, transform, and integrate structured and semi-structured data from databases, files, APIs, enterprise applications, and cloud platforms.
• Write clear, efficient, and maintainable SQL and Python code for data extraction, transformation, validation, and automation.
• Support the creation of reusable ETL/ELT components, data models, and curated datasets for analytics, reporting, and AI/ML use cases.
•  Implement data quality checks covering completeness, validity, consistency, uniqueness, and timeliness; investigate and help resolve data issues.
•  Prepare, clean, label, and version datasets required for machine-learning experimentation and model lifecycle activities.
• Work with data scientists, analysts, application teams, and business stakeholders to understand data requirements and translate them into technical tasks.
•  Create and maintain technical documentation, source-to-target mappings, data dictionaries, lineage information, operating procedures, and support notes.
• Use version control, code reviews, testing, and CI/CD practices to deliver reliable and traceable data engineering changes.
•  Assist with operational monitoring, incident analysis, root-cause identification, and continuous improvement of pipeline performance and reliability.
•  Follow enterprise requirements for data security, privacy, access control, retention, and responsible use of data and AI.
•  Continuously build knowledge of data engineering, cloud services, data architecture, analytics, and AI/ML technologies.

What should your background look like:

• Bachelor's degree in Artificial Intelligence & Machine Learning, Computer Science, Data Science, Information Technology, or a related engineering discipline, graduating in 2026.
• Strong academic or project-based foundation in Python and SQL, including data structures, object-oriented programming, query design, joins, aggregations, and performance basics.
• Understanding of relational databases, data warehousing concepts, dimensional modeling, normalization, and ETL/ELT fundamentals.
• Working knowledge of data-processing libraries such as pandas and NumPy, along with basic familiarity with scikit-learn or equivalent ML frameworks.
• Exposure through coursework, internship, certification, hackathon, or projects to at least one cloud platform such as Microsoft Azure, Amazon Web Services, or Google Cloud Platform.
•  Familiarity with Git or another version-control system and basic understanding of software development, unit testing, and CI/CD practices.
• Basic knowledge of data formats and interfaces such as CSV, JSON, Parquet, REST APIs, and file-based data exchange.
• Awareness of distributed data-processing or orchestration technologies such as Apache Spark, Databricks, Airflow, Azure Data Factory, or equivalent tools is preferred.
• Understanding of data quality, metadata, data lineage, security, privacy, and responsible AI principles.
• Demonstrated ability to learn quickly, analyze problems logically, communicate clearly, and collaborate effectively in a team.
• Academic project, internship, or portfolio demonstrating an end-to-end data pipeline, data platform, analytics solution, or ML data preparation workflow is preferred.
• Candidates should be able to explain their individual contribution to academic, internship, or portfolio projects and the engineering decisions made.

Competencies